Snowflake Data Analyst Case Interview — SQL, Semi-Structured Data, and Usage/Cost Insights in the Data Cloud

This case mirrors real Snowflake Data Analyst interviews, emphasizing practical SQL depth, product/business intuition, and clear, low‑ego collaboration. You’ll work through a realistic customer scenario using Snowflake‑style datasets that include both structured and semi‑structured data, then translate findings into crisp recommendations focused on customer value, performance, and credit spend. Interview flow (70 minutes): - 0–5 min: Context + expectations. Interviewer outlines a customer scenario: a fast‑growing client’s credit consumption and query latency have increased after onboarding a new event stream into the Data Cloud. - 5–10 min: Clarifying questions. Candidate probes for goals, SLAs, cost sensitivities, and success metrics. Snowflake style favors concise, customer‑first framing and humility in unknowns. - 10–30 min: SQL + data exploration (hands‑on or whiteboard). Candidate explores 3 Snowflake‑like tables/views: 1) events_raw (VARIANT) — semi‑structured user events with nested attributes; partitioned by event_date. 2) orders_fact — order_id, user_id, ts, amount, country, channel. 3) usage_views — rows modeled after ACCOUNT_USAGE (e.g., QUERY_HISTORY, WAREHOUSE_METERING_HISTORY) with warehouse_name, credits_used, query_id, query_type, queued_overload_time. Tasks (examples): - Parse JSON fields: extract event_name and device from events_raw using VARIANT and FLATTEN; join to orders_fact for conversion analysis. - Compute weekly active users (WAU), conversion rate, and revenue per active user; compare pre/post feature launch. - Identify top query patterns by latency and scan volume; attribute credit drivers by warehouse and workload. - Show window‑function proficiency (RANK, ROW_NUMBER, moving averages) and careful NULL/duplicate handling. - 30–50 min: Case analysis & recommendations. Translate findings into actions a Snowflake customer team could take: - Cost/perf levers: right‑size or auto‑suspend warehouses, consider multi‑cluster for concurrency spikes, prune scans via clustering/filters, materialize high‑value aggregates, and leverage result caching. - Data modeling: propose lightly modeled views on top of semi‑structured data; justify when to stage VARIANT vs. normalize. - Governance: suggest object tagging for cost allocation, RBAC hygiene, and using zero‑copy clones for safe experimentation. - Experimentation/metrics: define success metrics, guardrails, and a simple A/B or pre/post plan with clear attribution and risks. - 50–65 min: Executive‑style readout. Deliver a 3–5 minute narrative: what’s happening, why it matters to the customer, what to do next, and expected impact on credits, latency, and user outcomes. Snowflake looks for structured storytelling, brevity, and customer impact. - 65–70 min: Q&A + reflection. Discuss trade‑offs, edge cases (e.g., late‑arriving events, timezone alignment), and how you’d productionize analyses (dashboards, alerts, cost reporting). What the interviewer evaluates (Snowflake‑specific focus): - SQL mastery in a Snowflake context: joins, windows, CTEs, semi‑structured parsing (VARIANT, FLATTEN), thoughtful use of filters to minimize scans. - Data Cloud literacy: understanding of virtual warehouses, storage vs. compute, result cache, time travel/zero‑copy clones, and credit‑spend drivers. - Product and customer sense: can tie metrics to real customer value, propose pragmatic, low‑risk steps, and quantify impact. - Communication and collaboration style: concise, humble, customer‑first; explains trade‑offs and assumptions; invites feedback. What to prepare: practice complex SQL with window functions and JSON parsing; review Snowflake concepts (warehouses, credits, caching, time travel, data sharing); rehearse a brief business‑impact narrative tying analysis to cost/performance and customer outcomes.